Accurate prediction of pure component physiochemical properties is crucial for process integration

multiscale modelling

and optimization. In this work

an enhanced framework for pure component property prediction by using explainable machine learning methods is proposed. In this framework

the molecular representation method based on the connectivity matrix effectively considers atomic bonding relationships to automatically generate features. The supervised machine learning model random forest is applied for feature ranking and pooling. The adjusted

R

2

is introduced to penalize the inclusion of additional features

providing an assessment of the true contribution of features. The prediction results for normal boiling point (

T

b

)

liquid molar volume (

L

mv

)

critical temperature (

T

c

) and critical pressure (

P

c

) obtained using Artificial Neural Network and Gaussian Process Regression models confirm the accuracy of the molecular representation method. Comparison with GC based models shows that the root-mean-square error on the test set can be reduced by up to 83.8%. To enhance the interpretability of the model

a featu

re analysis method based on Shapley values is employed to determine the contribution of each feature to the property predictions. The results indicate that using the feature pooling method reduces the number of features from 13316 to 100 without compromising model accuracy. The feature analysis results for

T

b

L

mv

T

c

and

P

c

confirms that different molecular properties are influenced by different structural features

aligning with mechanistic interpretations. In conclusion

the proposed framework is demonstrated to be feasible and provides a solid foundation for mixture component reconstruction and process integration modelling.